[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

src/lerobot/envs/configs.py

@@ -272,6 +272,7 @@ class HILEnvConfig(EnvConfig):
             "gripper_penalty": self.gripper_penalty,
         }
 
+
 @EnvConfig.register_subclass("libero")
 @dataclass
 class LiberoEnv(EnvConfig):

src/lerobot/envs/libero.py

@@ -2,7 +2,8 @@ import math
 import os
 from collections import defaultdict
 from itertools import chain
-from typing import Any, Callable
+from typing import Any
+from collections.abc import Callable
 
 import gymnasium as gym
 import numpy as np

src/lerobot/scripts/eval.py

@@ -55,13 +55,12 @@ from copy import deepcopy
 from dataclasses import asdict
 from pathlib import Path
 from pprint import pformat
-from typing import Callable
+from collections.abc import Callable
 
 import einops
 import gymnasium as gym
 import numpy as np
 import torch
-from termcolor import colored
 from torch import Tensor, nn
 from tqdm import trange
 
@@ -73,7 +72,6 @@ from lerobot.policies.factory import make_policy
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.policies.utils import get_device_from_parameters
 from lerobot.utils.io_utils import write_video
-from lerobot.utils.random_utils import set_seed
 from lerobot.utils.utils import (
     get_safe_torch_device,
     init_logging,
@@ -456,6 +454,7 @@ def _compile_episode_data(
 
     return data_dict
 
+
 @parser.wrap()
 def eval(cfg: EvalPipelineConfig):
     logging.info(pformat(asdict(cfg)))
@@ -523,6 +522,7 @@ def eval(cfg: EvalPipelineConfig):
 
     logging.info("End of eval")
 
+
 def eval_policy_multitask(
     envs: dict[str, dict[str, gym.vector.VectorEnv]],
     policy,
@@ -545,7 +545,14 @@ def eval_policy_multitask(
         """Evaluates a single task in parallel."""
         print(f"Evaluating: task_group: {task_group}, task_id: {task_id} ...")
         task_result = eval_policy(
-            env, policy, n_episodes, max_episodes_rendered, videos_dir, return_episode_data, start_seed, verbose=verbose
+            env,
+            policy,
+            n_episodes,
+            max_episodes_rendered,
+            videos_dir,
+            return_episode_data,
+            start_seed,
+            verbose=verbose,
         )
 
         per_episode = task_result["per_episode"]
@@ -629,6 +636,7 @@ def eval_policy_multitask(
 
     return results
 
+
 if __name__ == "__main__":
     init_logging()
     eval_main()

src/lerobot/scripts/train.py

@@ -253,22 +253,22 @@ def train(cfg: TrainPipelineConfig):
                 torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext(),
             ):
                 if cfg.env.multitask_eval:
-                            eval_info = eval_policy_multitask(
-                                eval_env,
-                                policy,
-                                cfg.eval.n_episodes,
-                                videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_id}",
-                                max_episodes_rendered=4,
-                                start_seed=cfg.seed,
-                                max_parallel_tasks=cfg.env.max_parallel_tasks,
-                            )
-                            aggregated_results = eval_info["overall"]["aggregated"]
-                            # Print per-suite stats
-                            for task_group, task_group_info in eval_info.items():
-                                if task_group == "overall":
-                                    continue  # Skip the overall stats since we already printed it
-                                print(f"\nAggregated Metrics for {task_group}:")
-                                print(task_group_info["aggregated"])
+                    eval_info = eval_policy_multitask(
+                        eval_env,
+                        policy,
+                        cfg.eval.n_episodes,
+                        videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_id}",
+                        max_episodes_rendered=4,
+                        start_seed=cfg.seed,
+                        max_parallel_tasks=cfg.env.max_parallel_tasks,
+                    )
+                    aggregated_results = eval_info["overall"]["aggregated"]
+                    # Print per-suite stats
+                    for task_group, task_group_info in eval_info.items():
+                        if task_group == "overall":
+                            continue  # Skip the overall stats since we already printed it
+                        print(f"\nAggregated Metrics for {task_group}:")
+                        print(task_group_info["aggregated"])
                 else:
                     eval_info = eval_policy(
                         eval_env,